code: def test_ill_shape(self):
      set_determinism(seed=123)
      device = "cuda" if torch.cuda.is_available() else "cpu"
  
      # regression metrics to check + truth metric function in numpy
      metrics = [
          MeanSquaredError,
          MeanAbsoluteError,
          RootMeanSquaredError,
          partial(PeakSignalToNoiseRatio, max_val=1.0),
      ]
      basedim = 10
  
      # different shape for pred/target
      with self.assertRaises((AssertionError, ValueError)):
          in_tensor_a = torch.rand((basedim,)).to(device)
          in_tensor_b = torch.rand((basedim, basedim)).to(device)
          for mt_fn in metrics:
              mt_fn().update([in_tensor_a, in_tensor_b])
  
file_path: MONAI/tests/test_handler_regression_metrics.py
imports: from __future__ import annotations
  import unittest
  from functools import partial
  import numpy
  import torch
  from ignite.engine import Engine
  from monai.handlers import MeanAbsoluteError, MeanSquaredError, PeakSignalToNoiseRatio, RootMeanSquaredError
  from monai.utils import set_determinism
label_desc: Use image node_id 3 for calling the TestHandlerRegressionMetrics obj's underlying member method code with example usage: obj.test_ill_shape() without return types